Abstract: Real-world artificial intelligence (AI) applications often require multiple
agents to work in a collaborative effort. Efficient learning for intra-agent
communication and coordination is an indispensable step towards general AI. In
this paper, we take StarCraft combat game as the test scenario, where the task
is to coordinate multiple agents as a team to defeat their enemies. To maintain
a scalable yet effective communication protocol, we introduce a multiagent
bidirectionally-coordinated network (BiCNet ['bIknet]) with a vectorised
extension of actor-critic formulation. We show that BiCNet can handle different
types of combats under diverse terrains with arbitrary numbers of AI agents for
both sides. Our analysis demonstrates that without any supervisions such as
human demonstrations or labelled data, BiCNet could learn various types of
coordination strategies that is similar to these of experienced game players.
Moreover, BiCNet is easily adaptable to the tasks with heterogeneous agents. In
our experiments, we evaluate our approach against multiple baselines under
different scenarios; it shows state-of-the-art performance, and possesses
potential values for large-scale real-world applications.